Telco Use Cases

Data-driven insights can be used to differentiate when operating in a very competitive space. For example, customer churn is a critical metric for Telco companies, whether it relates to an account or a particular product or service. Recognising the factors that increase churn and identify customers at high risk provides the opportunity to take steps to prevent it.

Customer Insights

Operational Efficiencies

Data Monetization

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A Deeper Dive:

Churn Prediction and Prevention

Customer churn is a priority focus for all consumer brands but is heightened challenge for the telecoms industry where telecom regulations mean it is easier than ever for customers to switch providers, putting critical subscription revenue at risk.

Customer churnTelco providers run sophisticated analytical programs to identify consumers who may be at risk of churning. These will look at patterns of behaviour across basic factors such as analysing changes in customer spend levels, changes in product usage (data levels, call levels), length of contract lock-in, etc., through to more sophisticated analysis such as network effect – what effect does one customer in a network switching providers have on a second customer’s likelihood to switch?

The Challenge
Churn prediction requires significant analysis of customer behaviour and customer network behaviour to deliver optimal results. The process requires significant analysis of the data of your customers who have already churned and needs to take a significant longitudinal perspective to understand how churn behaviours progress over time and with respect to differing contract lengths.

GDPR and e-Privacy regulations can place significant limitations on a Telco’s ability to perform this type of analysis. Data protection law, and in particular the principle of data minimization only allows for personal data to be retained as long as is necessary. Even where churned customer data is retained for purposes such as fraud, AML or to comply with legal obligations, GDPR prevents the repurposing of this data for other uses. So if you cannot use the data of your churned customer's for analysis, how can you develop churn prevention models? In addition, regulations also impact Telcos’ ability to retain persistent identifiers over time for analysis purposes, making medium to long term longitudinal analysis challenging.

The Solution
By using the Trūata Anonymization Service a Telco can perform extensive longitudinal analysis and predictive modelling across their entire customer base, including churned customers. As both individual customers and all the related customers in their network are anonymized, data minimization does not apply and Trūata completely removes the heightened risk associated with more sophisticated levels of customer network analysis.

Telcos can be assured that they can run analysis over several years’ worth of data. Anonymized, persistent identifiers are maintained, allowing the Telco to perform behavioural analysis and train predictive models effectively over the full longitudinal view of data.

Intelligent Customer Journeys

Access to customer location data provides a unique opportunity to Telcos to drive operational efficiencies but also to generate new revenue streams through monetization of location-based services, particularly when blended with additional insights which the Telco has with respect to the customer.

location based dataUnderstanding customer journeys and linger times, such as what locations specific customer segments frequent at specific times, and predicting future traffic locations are of significant interest to traditional bricks and mortar retail businesses, media planning companies, event management companies, etc.

Compelling new product propositions can emerge from demographic and location-based insights such as “Where can I typically access the highest concentration of higher income 24 to 35-year olds on Saturdays between noon and 2pm?”.

The Challenge
The use of location data is heavily regulated under GDPR and e-Privacy regulations. Telcos are restricted by law in using this data where it is not necessary for conveyance of communications.

The Solution
Using the Trūata Anonymization Service and the privacy controls that we apply to data insights generation, location data can safely be used to gain new insights and monetize data.

The service anonymizes location data so that maximum utility is maintained to support your analytical and data insight use cases. Privacy controls and tests ensures that data insights for aggregated customer segments based on the location data will only be generated where the re-identification of a data subject is not possible.

Follow the links below to learn more about the use cases for the Trūata Anonymization Service.

Automotive     Financial Services     Retail     Travel and Hospitality